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Tae Sun Yoo Department of Medical Biophysics University of Western Ontario Supervisor: Dr. Keith St Lawrence and Ph.D. candidate Jonathan Elliott.

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Presentation on theme: "Tae Sun Yoo Department of Medical Biophysics University of Western Ontario Supervisor: Dr. Keith St Lawrence and Ph.D. candidate Jonathan Elliott."— Presentation transcript:

1 Tae Sun Yoo Department of Medical Biophysics University of Western Ontario Supervisor: Dr. Keith St Lawrence and Ph.D. candidate Jonathan Elliott

2 Near Infrared Spectroscopy & Principles Near infrared spectroscopy is a powerful optical technique used to measure cerebral blood flow (CBF) and cerebral blood oxygenation. 1)Bolus injection light-absorbing dye, indocyanine green (ICG) 2)Monitor passage of ICG through brain by NIRS 3) CBF determined by shape of time- concentration data Blood Flow Technique:

3 Clinical Relevance of NIRS Brain injury is a leading cause of deaths and disability in Canada. NIRS can be used to assess brain health at the bedside of head-trauma patients NIRS works well with infants, but not with adults Problem is signal contamination from extracerebral layer Consequence is reduced sensitivity to brain leading to underestimation of CBF.

4 Approach MRIcro was used to import MRI images and Image J was used to obtain length of extracerebral layer (ECL) thickness values across the circumference of the head (from 0 to 360˚)

5 Hypothesis Variations in ECL thickness across the subject will be sufficient to affect the NIRS brain attenuation signal to measure accurate cerebral blood flow (CBF).

6 Method Brain MRI images were acquired from five young healthy subjects & viewed by MRIcro. Anterior Commissure (AC) was chosen as the reference location for all subjects MRI images were exported in 2 transverse slices per subject.

7 Method Brain MRI images were loaded onto “ImageJ”. By using a line tool and fill function, outlines were drawn from 0 to 360˚ within the image. Color picker function was used with pencil tool. land markers were made at two locations. Total (ECL) thickness measurements were done by moving a line tool at 10˚increment with use of measurement function.

8 Scalp Skull Angle (θ) from 0° to 360° Record: Total ECL thickness made by Δ10° increment 0, 360˚ 90˚ 180 ˚ 270˚ Angle (θ) Position Note: MR images were calibrated by 1 x 1 mmECL thickness

9 Results

10 *p < 0.05 ∆ in ECL thickness between AC & AC +20mm:  ECL thickness was slightly thinner at AC above 20mm!

11  As ECL thickness increased, % of brain signal is reduced Subject 3, AC, Region I 9%

12 Mean, AC+20, Region I Brain contribution: 35% Subject 3, AC, Region I Brain contribution: 9% Pure brain curve Pure scalp curve ICG concentration ( uM) Time (s) ICG concentration ( uM) Time (s) ICG concentration ( uM)

13 Discussion Main limitations: There isn’t a decent imaging tool which resolves different ECL layers separately. Source of errors:. Small sample size (5 subjects). Resolution of MR image quality (reduced with “zoom in” function)

14 Discussion Future works on identifying ECL thickness variability: By standardizing variation of ECL thickness, more precise and reliable CBF measurements can be made on adults. In near future, individualized approach on making an adjustment on CBF measurements are possible by removing ECL (skull and scalp) contamination.

15 Conclusion Mean ECL thickness across the circumference from subject 1 to 5 at AC+20mm was thinner by 2mm than at AC. Based on regions (I-IV), forehead region I at AC above 20mm was shown to be the thinnest mean ECL thickness. Thus, thinner the ECL thickness, better the CBF measurements by increased in contribution of brain signal (more light propagates into brain and reduced ECL contamination).


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